# Tagged with #rnaseq 2 documentation articles | 8 announcements | 23 forum discussions

Created 2014-04-16 22:09:49 | Updated 2015-05-16 06:58:15 | Tags: best-practices rnaseq

This is our recommended workflow for calling variants in RNAseq data from single samples, in which all steps are performed per-sample. In future we will provide cohort analysis recommendations, but these are not yet available.

The workflow is divided in three main sections that are meant to be performed sequentially:

• Variant discovery: from reads (BAM files) to variants (VCF files)
• Refinement and evaluation: genotype refinement, functional annotation and callset QC

Compared to the DNAseq Best Practices, the key adaptations for calling variants in RNAseq focus on handling splice junctions correctly, which involves specific mapping and pre-processing procedures, as well as some new functionality in the HaplotypeCaller, which are highlighted in the figure below.

### Pre-Processing

The data generated by the sequencers are put through some pre-processing steps to make it suitable for variant calling analysis. The steps involved are: Mapping and Marking Duplicates; Split'N'Trim; Local Realignment Around Indels (optional); and Base Quality Score Recalibration (BQSR); performed in that order.

#### Mapping and Marking Duplicates

The sequence reads are first mapped to the reference using STAR aligner (2-pass protocol) to produce a file in SAM/BAM format sorted by coordinate. The next step is to mark duplicates. The rationale here is that during the sequencing process, the same DNA molecules can be sequenced several times. The resulting duplicate reads are not informative and should not be counted as additional evidence for or against a putative variant. The duplicate marking process identifies these reads as such so that the GATK tools know they should ignore them.

#### Split'N'Trim

Then, an RNAseq-specific step is applied: reads with N operators in the CIGAR strings (which denote the presence of a splice junction) are split into component reads and trimmed to remove any overhangs into splice junctions, which reduces the occurrence of artifacts. At this step, we also reassign mapping qualities from 255 (assigned by STAR) to 60 which is more meaningful for GATK tools.

#### Realignment Around Indels

Next, local realignment is performed around indels, because the algorithms that are used in the initial mapping step tend to produce various types of artifacts. For example, reads that align on the edges of indels often get mapped with mismatching bases that might look like evidence for SNPs, but are actually mapping artifacts. The realignment process identifies the most consistent placement of the reads relative to the indel in order to clean up these artifacts. It occurs in two steps: first the program identifies intervals that need to be realigned, then in the second step it determines the optimal consensus sequence and performs the actual realignment of reads. This step is considered optional for RNAseq.

#### Base Quality Score Recalibration

Finally, base quality scores are recalibrated, because the variant calling algorithms rely heavily on the quality scores assigned to the individual base calls in each sequence read. These scores are per-base estimates of error emitted by the sequencing machines. Unfortunately the scores produced by the machines are subject to various sources of systematic error, leading to over- or under-estimated base quality scores in the data. Base quality score recalibration is a process in which we apply machine learning to model these errors empirically and adjust the quality scores accordingly. This yields more accurate base qualities, which in turn improves the accuracy of the variant calls. The base recalibration process involves two key steps: first the program builds a model of covariation based on the data and a set of known variants, then it adjusts the base quality scores in the data based on the model.

### Variant Discovery

Once the data has been pre-processed as described above, it is put through the variant discovery process, i.e. the identification of sites where the data displays variation relative to the reference genome, and calculation of genotypes for each sample at that site. Because some of the variation observed is caused by mapping and sequencing artifacts, the greatest challenge here is to balance the need for sensitivity (to minimize false negatives, i.e. failing to identify real variants) vs. specificity (to minimize false positives, i.e. failing to reject artifacts). It is very difficult to reconcile these objectives in a single step, so instead the variant discovery process is decomposed into separate steps: variant calling (performed per-sample) and variant filtering (also performed per-sample). The first step is designed to maximize sensitivity, while the filtering step aims to deliver a level of specificity that can be customized for each project.

Our current recommendation for RNAseq is to run all these steps per-sample. At the moment, we do not recommend applying the GVCF-based workflow to RNAseq data because although there is no obvious obstacle to doing so, we have not validated that configuration. Therefore, we cannot guarantee the quality of results that this would produce.

#### Per-Sample Variant Calling

We perform variant calling by running the HaplotypeCaller on each sample BAM file (if a sample's data is spread over more than one BAM, then pass them all in together) to create single-sample VCFs containing raw SNP and indel calls.

#### Per-Sample Variant Filtering

For RNAseq, it is not appropriate to apply variant recalibration in its present form. Instead, we provide hard-filtering recommendations to filter variants based on specific annotation value thresholds. This produces a VCF of calls annotated with fiiltering information that can then be used in downstream analyses.

### Refinement and evaluation

In this last section, we perform some refinement steps on the genotype calls (GQ estimation and transmission phasing), add functional annotations if desired, and do some quality evaluation by comparing the callset to known resources. None of these steps are absolutely required, and the workflow may need to be adapted quite a bit to each project's requirements.

Important note on GATK versions The [Best Practices](http://www.broadinstitute.org/gatk/guide/best-practices) have been updated for GATK version 3. If you are running an older version, you should seriously consider upgrading. For more details about what has changed in each version, please see the [Version History](http://www.broadinstitute.org/gatk/guide/version-history) section. If you cannot upgrade your version of GATK for any reason, please look up the corresponding version of the GuideBook PDF (also in the [Version History](http://www.broadinstitute.org/gatk/guide/version-history) section) to ensure that you are using the appropriate recommendations for your version.

Created 2014-03-06 07:15:51 | Updated 2015-12-07 11:08:30 | Tags: best-practices rnaseq

### Overview

This document describes the details of the GATK Best Practices workflow for SNP and indel calling on RNAseq data.

Please note that any command lines are only given as example of how the tools can be run. You should always make sure you understand what is being done at each step and whether the values are appropriate for your data. To that effect, you can find more guidance here.

In brief, the key modifications made to the DNAseq Best Practices focus on handling splice junctions correctly, which involves specific mapping and pre-processing procedures, as well as some new functionality in the HaplotypeCaller. Here is a detailed overview:

### Caveats

Please keep in mind that our DNA-focused Best Practices were developed over several years of thorough experimentation, and are continuously updated as new observations come to light and the analysis methods improve. We have been working with RNAseq for a somewhat shorter time, so there are many aspects that we still need to examine in more detail before we can be fully confident that we are doing the best possible thing.

We know that the current recommended pipeline is producing both false positives (wrong variant calls) and false negatives (missed variants) errors. While some of those errors are inevitable in any pipeline, others are errors that we can and will address in future versions of the pipeline. A few examples of such errors are given in this article as well as our ideas for fixing them in the future.

We will be improving these recommendations progressively as we go, and we hope that the research community will help us by providing feedback of their experiences applying our recommendations to their data.

### The workflow

#### 1. Mapping to the reference

The first major difference relative to the DNAseq Best Practices is the mapping step. For DNA-seq, we recommend BWA. For RNA-seq, we evaluated all the major software packages that are specialized in RNAseq alignment, and we found that we were able to achieve the highest sensitivity to both SNPs and, importantly, indels, using STAR aligner. Specifically, we use the STAR 2-pass method which was described in a recent publication (see page 43 of the Supplemental text of the Pär G Engström et al. paper referenced below for full protocol details -- we used the suggested protocol with the default parameters). In brief, in the STAR 2-pass approach, splice junctions detected in a first alignment run are used to guide the final alignment.

Here is a walkthrough of the STAR 2-pass alignment steps:

1) STAR uses genome index files that must be saved in unique directories. The human genome index was built from the FASTA file hg19.fa as follows:

genomeDir=/path/to/hg19
mkdir $genomeDir STAR --runMode genomeGenerate --genomeDir$genomeDir --genomeFastaFiles hg19.fa\  --runThreadN <n>

2) Alignment jobs were executed as follows:

runDir=/path/to/1pass
mkdir $runDir cd$runDir
STAR --genomeDir $genomeDir --readFilesIn mate1.fq mate2.fq --runThreadN <n> 3) For the 2-pass STAR, a new index is then created using splice junction information contained in the file SJ.out.tab from the first pass: genomeDir=/path/to/hg19_2pass mkdir$genomeDir
STAR --runMode genomeGenerate --genomeDir genomeDir --genomeFastaFiles hg19.fa \ --sjdbFileChrStartEnd /path/to/1pass/SJ.out.tab --sjdbOverhang 75 --runThreadN <n> 4) The resulting index is then used to produce the final alignments as follows: runDir=/path/to/2pass mkdirrunDir
cd $runDir STAR --genomeDir$genomeDir --readFilesIn mate1.fq mate2.fq --runThreadN <n>

The above step produces a SAM file, which we then put through the usual Picard processing steps: adding read group information, sorting, marking duplicates and indexing.

java -jar picard.jar AddOrReplaceReadGroups I=star_output.sam O=rg_added_sorted.bam SO=coordinate RGID=id RGLB=library RGPL=platform RGPU=machine RGSM=sample

java -jar picard.jar MarkDuplicates I=rg_added_sorted.bam O=dedupped.bam  CREATE_INDEX=true VALIDATION_STRINGENCY=SILENT M=output.metrics 

#### 3. Split'N'Trim and reassign mapping qualities

Next, we use a new GATK tool called SplitNCigarReads developed specially for RNAseq, which splits reads into exon segments (getting rid of Ns but maintaining grouping information) and hard-clip any sequences overhanging into the intronic regions.

In the future we plan to integrate this into the GATK engine so that it will be done automatically where appropriate, but for now it needs to be run as a separate step.

At this step we also add one important tweak: we need to reassign mapping qualities, because STAR assigns good alignments a MAPQ of 255 (which technically means “unknown” and is therefore meaningless to GATK). So we use the GATK’s ReassignOneMappingQuality read filter to reassign all good alignments to the default value of 60. This is not ideal, and we hope that in the future RNAseq mappers will emit meaningful quality scores, but in the meantime this is the best we can do. In practice we do this by adding the ReassignOneMappingQuality read filter to the splitter command.

Please note that we recently (6/11/14) edited this to fix a documentation error regarding the filter to use. See this announcement for details.

Finally, be sure to specify that reads with N cigars should be allowed. This is currently still classified as an "unsafe" option, but this classification will change to reflect the fact that this is now a supported option for RNAseq processing.

java -jar GenomeAnalysisTK.jar -T SplitNCigarReads -R ref.fasta -I dedupped.bam -o split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS

#### 4. Indel Realignment (optional)

After the splitting step, we resume our regularly scheduled programming... to some extent. We have found that performing realignment around indels can help rescue a few indels that would otherwise be missed, but to be honest the effect is marginal. So while it can’t hurt to do it, we only recommend performing the realignment step if you have compute and time to spare (or if it’s important not to miss any potential indels).

#### 5. Base Recalibration

We do recommend running base recalibration (BQSR). Even though the effect is also marginal when applied to good quality data, it can absolutely save your butt in cases where the qualities have systematic error modes.

Both steps 4 and 5 are run as described for DNAseq (with the same known sites resource files), without any special arguments. Finally, please note that you should NOT run ReduceReads on your RNAseq data. The ReduceReads tool will no longer be available in GATK 3.0.

#### 6. Variant calling

Finally, we have arrived at the variant calling step! Here, we recommend using HaplotypeCaller because it is performing much better in our hands than UnifiedGenotyper (our tests show that UG was able to call less than 50% of the true positive indels that HC calls). We have added some functionality to the variant calling code which will intelligently take into account the information about intron-exon split regions that is embedded in the BAM file by SplitNCigarReads. In brief, the new code will perform “dangling head merging” operations and avoid using soft-clipped bases (this is a temporary solution) as necessary to minimize false positive and false negative calls. To invoke this new functionality, just add -dontUseSoftClippedBases to your regular HC command line. Note that the -recoverDanglingHeads argument which was previously required is no longer necessary as that behavior is now enabled by default in HaplotypeCaller. Also, we found that we get better results if we lower the minimum phred-scaled confidence threshold for calling variants on RNAseq data, so we use a default of 20 (instead of 30 in DNA-seq data).

java -jar GenomeAnalysisTK.jar -T HaplotypeCaller -R ref.fasta -I input.bam -dontUseSoftClippedBases -stand_call_conf 20.0 -stand_emit_conf 20.0 -o output.vcf

#### 7. Variant filtering

To filter the resulting callset, you will need to apply hard filters, as we do not yet have the RNAseq training/truth resources that would be needed to run variant recalibration (VQSR).

We recommend that you filter clusters of at least 3 SNPs that are within a window of 35 bases between them by adding -window 35 -cluster 3 to your command. This filter recommendation is specific for RNA-seq data.

As in DNA-seq, we recommend filtering based on Fisher Strand values (FS > 30.0) and Qual By Depth values (QD < 2.0).

java -jar GenomeAnalysisTK.jar -T VariantFiltration -R hg_19.fasta -V input.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o output.vcf 

Please note that we selected these hard filtering values in attempting to optimize both high sensitivity and specificity together. By applying the hard filters, some real sites will get filtered. This is a tradeoff that each analyst should consider based on his/her own project. If you care more about sensitivity and are willing to tolerate more false positives calls, you can choose not to filter at all (or to use less restrictive thresholds).

An example of filtered (SNPs cluster filter) and unfiltered false variant calls:

An example of true variants that were filtered (false negatives). As explained in text, there is a tradeoff that comes with applying filters:

### Known issues

There are a few known issues; one is that the allelic ratio is problematic. In many heterozygous sites, even if we can see in the RNAseq data both alleles that are present in the DNA, the ratio between the number of reads with the different alleles is far from 0.5, and thus the HaplotypeCaller (or any caller that expects a diploid genome) will miss that call. A DNA-aware mode of the caller might be able to fix such cases (which may be candidates also for downstream analysis of allele specific expression).

Although our new tool (splitNCigarReads) cleans many false positive calls that are caused by splicing inaccuracies by the aligners, we still call some false variants for that same reason, as can be seen in the example below. Some of those errors might be fixed in future versions of the pipeline with more sophisticated filters, with another realignment step in those regions, or by making the caller aware of splice positions.

As stated previously, we will continue to improve the tools and process over time. We have plans to improve the splitting/clipping functionalities, improve true positive and minimize false positive rates, as well as developing statistical filtering (i.e. variant recalibration) recommendations.

We also plan to add functionality to process DNAseq and RNAseq data from the same samples simultaneously, in order to facilitate analyses of post-transcriptional processes. Future extensions to the HaplotypeCaller will provide this functionality, which will require both DNAseq and RNAseq in order to produce the best results. Finally, we are also looking at solutions for measuring differential expression of alleles.

[1] Pär G Engström et al. “Systematic evaluation of spliced alignment programs for RNA-seq data”. Nature Methods, 2013

Created 2015-12-08 08:22:39 | Updated 2015-12-09 18:42:17 | Tags: workshop presentations rnaseq slides

The hands-on tutorial files and presentations slides from the Dec 8, 2015 workshop at VIB in Gent, Belgium are available at this link:

Created 2014-08-15 09:41:46 | Updated | Tags: appistry rnaseq webinar

Our partners at Appistry will be doing a webinar on RNAseq analysis next Thursday. The webinar will include a live presentation of the complete pipeline for RNAseq analysis, as well as question time open to all participants. As usual it's free and open to all, you just need to register at Appistry's website. Check it out!

Created 2014-06-11 21:20:08 | Updated | Tags: best-practices bug error rnaseq topstory

We discovered today that we made an error in the documentation article that describes the RNAseq Best Practices workflow. The error is not critical but is likely to cause an increased rate of False Positive calls in your dataset.

The error was made in the description of the "Split & Trim" pre-processing step. We originally wrote that you need to reassign mapping qualities to 60 using the ReassignMappingQuality read filter. However, this causes all MAPQs in the file to be reassigned to 60, whereas what you want to do is reassign MAPQs only for good alignments which STAR identifies with MAPQ 255. This is done with a different read filter, called ReassignOneMappingQuality. The correct command is therefore:

java -jar GenomeAnalysisTK.jar -T SplitNCigarReads -R ref.fasta -I dedupped.bam -o split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS

In our hands we see a bump in the rate of FP calls from 4% to 8% when the wrong filter is used. We don't see any significant amount of false negatives (lost true positives) with the bad command, although we do see a few more true positives show up in the results of the bad command. So basically the effect is to excessively increase sensitivity, at the expense of specificity, because poorly mapped reads are taken into account with a "good" mapping quality, where they would normally be discarded.

This effect will be stronger in datasets with lower overall quality, so your results may vary. Let us know if you observe any really dramatic effects, but we don't expect that to happen.

To be clear, we do recommend re-processing your data if you can, but if that is not an option, keep in mind how this affects the rate of false positive discovery in your data.

We apologize for this error (which has now been corrected in the documentation) and for the inconvenience it may cause you.

Created 2014-04-04 01:48:46 | Updated | Tags: appistry rnaseq webinar gvcf gatk3

Our partners at Appistry are putting on another webinar next week, and this one's going to be pretty special in our view -- because we're going to be doing pretty much all the talking!

Titled "Speed, Cohorts, and RNAseq: An Insider Look into GATK 3" (see that link for the full program), this webinar will be all about the GATK 3 features, of course. And lest you think this is just another marketing pitch (no offense, marketing people), rest assured that we will be diving into the gory technical details of what happens under the hood. This is a great opportunity to get the inside scoop on how the new features (RNAseq, GVCF pipeline etc) work -- all the stuff that's fit to print, but that we haven't had time to write down in the docs yet. So don't miss it if that's the sort of thing that floats your boat! Or if you miss it, be sure to check out the recording afterward.

As usual the webinar is completely free and open to everyone (not just Appistry customers or prospective for-profit users). All you need to do is register now and tune in on Thursday 4/10.

Talk to you then!

Created 2014-03-17 23:32:16 | Updated | Tags: release rnaseq version-highlights multisample reference-model joint-analysis

Better late than never, here is the now-traditional "Highlights" document for GATK version 3.0, which was released two weeks ago. It will be a very short one since we've already gone over the new features in detail in separate articles --but it's worth having a recap of everything in one place. So here goes.

### Work smarter, not harder

We are delighted to present our new Best Practices workflow for variant calling in which multisample calling is replaced by a winning combination of single-sample calling in gVCF mode and joint genotyping analysis. This allows us to both bypass performance issues and solve the so-called "N+1 problem" in one fell swoop. For full details of why and how this works, please see this document. In the near future, we will update our Best Practices page to make it clear that the new workflow is now the recommended way to go for calling variants on cohorts of samples. We've already received some pretty glowing feedback from early adopters, so be sure to try it out for yourself!

### Jumping on the RNAseq bandwagon

All the cool kids were doing it, so we had to join the party. It took a few months of experimentation, a couple of new tools and some tweaks to the HaplotypeCaller, but you can now call variants on RNAseq with GATK! This document details our Best Practices recommendations for doing so, along with a non-trivial number of caveats that you should keep in mind as you go.

Nice try, but no. This tool is obsolete now that we have the gVCF/reference model pipeline (see above). Note that this means that GATK 3.0 will not support BAM files that were processed using ReduceReads!

### Changes for developers

We've switched the build system from Ant to Maven, which should make it much easier to use GATK as a library against which you can develop your own tools. And on a related note, we're also making significant changes to the internal structure of the GATK codebase. Hopefully this will not have too much impact on external projects, but there will be a doc very shortly describing how the new build system works and how the codebase is structured.

### Hardware optimizations held for 3.1

For reasons that will be made clear in the near future, we decided to hold the previously announced hardware optimizations until version 3.1, which will be released very soon. Stay tuned!

Created 2014-03-06 07:24:03 | Updated | Tags: best-practices rnaseq topstory

We’re excited to introduce our Best Practices recommendations for calling variants on RNAseq data. These recommendations are based on our classic DNA-focused Best Practices, with some key differences in the early data processing steps, as well as in the calling step.

### Best Practices workflow for RNAseq

This workflow is intended to be run per-sample; joint calling on RNAseq is not supported yet, though that is on our roadmap.

Please see the new document here for full details about how to run this workflow in practice.

In brief, the key modifications made to the DNAseq Best Practices focus on handling splice junctions correctly, which involves specific mapping and pre-processing procedures, as well as some new functionality in the HaplotypeCaller.

Now, before you try to run this on your data, there are a few important caveats that you need to keep in mind.

Please keep in mind that our DNA-focused Best Practices were developed over several years of thorough experimentation, and are continuously updated as new observations come to light and the analysis methods improve. We have only been working with RNAseq for a few months, so there are many aspects that we still need to examine in more detail before we can be fully confident that we are doing the best possible thing.

For one thing, these recommendations are based on high quality RNA-seq data (30 million 75bp paired-end reads produced on Illumina HiSeq). Other types of data might need slightly different processing. In addition, we have currently worked only on data from one tissue from one individual. Once we’ve had the opportunity to get more experience with different types (and larger amounts) of data, we will update these recommendations to be more comprehensive.

Finally, we know that the current recommended pipeline is producing both false positives (wrong variant calls) and false negatives (missed variants) errors. While some of those errors are inevitable in any pipeline, others are errors that we can and will address in future versions of the pipeline. A few examples of such errors are given in this article as well as our ideas for fixing them in the future.

We will be improving these recommendations progressively as we go, and we hope that the research community will help us by providing feedback of their experiences applying our recommendations to their data. We look forward to hearing your thoughts and observations!

Created 2014-02-24 13:46:45 | Updated 2014-02-24 13:49:40 | Tags: rnaseq multisample topstory pairhmm

Previously, we covered the spirit of GATK 3.0 (what our intentions are for this new release, and what we’re hoping to achieve). Let’s now have a look at the top three features you can look forward to in 3.0, in no particular order:

1. Optimized PairHMM algorithm to make GATK run faster
2. Single-sample pipeline for joint variant discovery
3. Best practices for calling variants on RNAseq data

### 1. Optimized PairHMM algorithm to make HaplotypeCaller faster

At this point everyone knows that the HaplotypeCaller is fabulous (you know this, right?) but beyond a certain number of samples that you’re trying to call jointly, it just grinds to a crawl, and any further movement is on the scale of continental drift. Obviously this is a major obstacle if you’re trying to do any kind of work at scale beyond a handful of samples, and that’s why it hasn’t been used in recent large-cohort projects despite showing best-in-class performance in terms of discovery power.

The major culprit in this case is the PairHMM algorithm, which takes up the lion’s share of HC runtime. With the help of external collaborators (to be credited in a follow-up post) we rewrote the code of the PairHMM to make it orders of magnitude faster, especially on specialized hardware like GPU and FPGA chips (but you’ll still see a speedup on “regular” hardware).

We plan to follow up on this by doing similar optimizations on the other “slowpoke” algorithms that are responsible for long runtimes in GATK tools.

### 2. Single-sample pipeline for joint variant discovery

Some problems in variant calling can’t be solved by Daft Punk hardware upgrades (better faster stronger) alone. Beyond the question of speed, a major issue with multi-sample variant discovery is that you have to wait until all the samples are available to call variants on them. Then, if later you want to add some more samples to your cohort, you have to re-call all of them together, old and new. This, also known as the “N+1 problem”, is a huge pain in the anatomy.

The underlying idea of the “single-sample pipeline for joint variant discovery” is to decouple the two steps in the variant calling process: identifying evidence of variation, and interpreting the evidence. Only the second step needs to be done jointly on all samples, while the first step can be done just as well (and a heck of a lot faster) on one sample at a time.

The new pipeline allows us to process each sample as it comes off the sequencing machine, up to the first step of variant calling. Cumulatively, this will produce a database of per-sample, per-site allele frequencies. Then it’s just a matter of running a joint analysis on the database, which can be done incrementally each time a new sample is added or at certain intervals or timepoints, depending on the research needs, because this step runs quickly and cheaply.

We’ll go into the details of exactly how this works in a follow-up post. For now, the take-home message is that it’s a “single-sample pipeline” because you do the heavy-lifting per-sample (and just once, ever), but you are empowered to perform “joint discovery” because you interpret the evidence from each sample in light of what you see in all the other samples, and you can do this at any point in the project timeline.

### 3. Best practices for calling variants on RNAseq

Our Best Practices recommendations for calling variants on DNA sequence data have proved to be wildly popular with the scientific community, presumably because it takes a lot of the guesswork out of running GATK, and provides a large degree of reproducibility.

Now, we’re excited to introduce our Best Practices recommendations for calling variants on RNAseq data. These recommendations are based on our classic DNA-focused Best Practices, with some key differences the early data processing steps, as well as in the calling step. We do not yet have RNAseq-specific recommendations for variant filtering/recalibration, but will be developing those in the coming weeks.

We’ll go into the details of the RNAseq Best Practices in a follow-up post, but in a nutshell, these are the key differences: use STAR for alignment, add an exon splitting and cleanup step, and tell the variant caller to take the splits into account. The latter involves some new code added to the variant callers; it is available to both HaplotypeCaller and UnifiedGenotyper, but UG is currently missing a whole lot of indels, so we do recommend using only HC in the immediate future.

Keep in mind that our DNA-focused Best Practices were developed over several years of thorough experimentation, and are continuously updated as new observations come to light and the analysis methods improve. We have only been working with RNAseq for a few months, so there are many aspects that we still need to examine in more detail before we can be fully confident that we are doing the best possible thing. We will be improving these recommendations progressively as we go, and we hope that the researcher community will help us by providing feedback of their experiences applying our recommendations to their data.

Created 2014-02-12 02:49:21 | Updated 2014-02-12 03:15:29 | Tags: rnaseq topstory joint-discovery

Yep, you read that right, the next release of GATK is going to be the big Three-Oh!

You may have noticed that the 2.8 release was really slim. We explained in the release notes, perhaps a tad defensively, that it was because we’d been working on some ambitious new features that just weren’t ready for prime time. And that was true. Now we’ve got a couple of shiny new toys to show for it that we think you’re really going to like.

But GATK 3.0 is not really about the new features (otherwise we’d just call it 2.9). It’s about a shift in the way we approach the problems that we want to solve -- and to some extent, a shift in the scope of problems we choose to tackle.

We’ll explain what this entails in much more detail in a series of blog posts over the next few days, but let me reassure you right now on one very important point: there is nothing in the upcoming release that will disrupt your existing workflows. What it will do is offer you new paths for discovery that we believe will empower research on a scale that has previously not been possible.

And lest you think this is all just vaporware, here’s a sample of what we have in hand right now: variant calling on RNA-Seq, and a multisample variant discovery workflow liberated from the shackles of time and scaling issues.

Stay tuned for details!

Created 2015-09-17 00:02:05 | Updated 2015-09-17 00:02:45 | Tags: haplotypecaller rnaseq gatk-walkers

Hi, I have been running HP for my RNA-seq data

java -Xmx16g -jar GenomeAnalysisTK.jar \ -T HaplotypeCaller \ -R $ref \ -I INPUT.bam \ -stand_call_conf 50.0 \ -stand_emit_conf 10.0 \ -o output.vcf my process is killed when it was 82% completed. Is there a way to resume the run without running from the beginning ? Thanks Best Regards T. Hamdi Kitapci Created 2015-09-14 16:27:46 | Updated | Tags: haplotypecaller vcf bam rnaseq variant-calling Hello, I'm using GATK to call variants in my RNA-Seq data. I'm noticing something strange, perhaps someone can help? For a number of sites the VCF is reporting things I cannot replicate from BAMs. How can I recover the reads that contribute to a variant call? Here is an example for 1 site in 1 sample, but I've observed this at many sites/samples: $ grep 235068463 file.vcf
chr1    235068463   .   T   C   1795.77 .   AC=1;AF=0.500;AN=2;BaseQRankSum=-3.530;ClippingRankSum=-0.535;DP=60;FS=7.844;MLEAC=1;MLEAF=0.500;MQ=60.00;MQ0=0;MQRankSum=0.401;QD=29.93;ReadPosRankSum=3.557   GT:AD:DP:GQ:PL  0/1:5,55:60:44:1824,0,44

60 reads, 5 T, 55 C.

samtools view -uh file.md.realn.bam chr1:235068463-235068463 |samtools mpileup - |grep 235068463 [mpileup] 1 samples in 1 input files <mpileup> Set max per-file depth to 8000 chr1 235068463 N 60 cCCccccCCCcccccCcccccccccCCCccCCCCCcCcccccCCCcCcCCccCCCCccCC >CA@B@>A>BA@BCABACCC:@@ACABBBCAACBBCABCB@CABBAB?>A?CBBAAAABA There are just 60 C's at that location. How do I decide what the genotype here is? C/C or C/T ? For methodology I'm using gatk/3.2.0. I tried using HC from gatk/3.3.1 and got the same result. The bam and vcf files come from the final two lines: -2 pass STAR -Mark Dups -SplitNCigarReads -RealignerTargetCreator -IndelRealigner -BaseRecalibrator -PrintReads -MergeSamFiles.jar -Mark Dups -RealignerTargetCreator -IndelRealigner -HaplotyeCaller Thanks, Kipp Created 2015-06-26 11:26:50 | Updated | Tags: bqsr knownsites rnaseq Hi, i read the entry about how to do BQSR without a knownSNP file and have some uncertainties how to apply it to RNAseq data. I am actually calling SNPs from RNA-seq data on a draft genome of a non-model organism and were wondering what best practice might be to do so (must sound like a nightmare to you working with human data :smile: ). I can think of following workflow for each of the RNA-seq samples: 1. best practice SNPcalling for RNA reads with HaplotypeCaller 2. filter variants for "high quality" (-window 25 -cluster 3 --filterExpression "MQ < 30.0" --filterExpression "QD < 2.0" --filterExpression "DP < 5" 3. select for PASS SNPs and biallelic SNPs (as sample is diploid) 4. use the selected SNPs as knownSNPs to do BQSR 5. run Haplotypecaller again on the recalibrated bam 6. go nuts with the gained vcf file... =) Should i include heterozygous SNPs to generate the BQSR-recalibration file? Would you agree on that workflow or alter the filters ( i know filtering for depth is not a good thing to do but for RNA-seq i think its good to have some minimal coverage of a site) Comments and recommendations are very welcome, Thank you, Michel Created 2015-06-24 18:55:35 | Updated | Tags: haplotypecaller multi-sample rnaseq pooled-calls Hi everybody! I've recently started working with GATK and after reading documents, tutorial and forum discussions, I set a pipeline for my experiment. I'm dealing with multi-sample RNAseq for which GATK tools are less improved and verified than DNAseq, thus I 'd like to have your suggestions. Briefly, this is the experiment: 2 phenotypes of Sparus aurata, 8 libary per phenotype, each library consists on a pool (not barcoded) of 3 animals. Thus I have a total of 16 samples. My goal is to find the total number of variant sites and compare the allele frequencies between the two phenotypes. I lack genome and SNPs database. Step by step: 1) I used STAR (not 2-pass) in order to map reads against my de novo assembly. STAR --runThreadN 16 --genomeDir ./GenomeIndex --readFilesIn XXX.fastq --alignIntronMax 19 --outSAMtype BAM SortedByCoordinate --outSAMmapqUnique 60 --outFilterMultimapNmax 5 --outFilterMismatchNmax 4 2) I used the picard-tools to Mark duplicates 3) I used the picard-tools to AddOrReplaceReadGroups 4) I used the picard-tools to BuildBamIndex 5) I called the haplotype for the 16 samples with the following command: GenomeAnalysisTK.jar -T HaplotypeCaller -R reference.fasta -I sample.bam -dontUseSoftClippedBases -ploidy 6 -ERC GVCF -o output.g.vcf 6) I used the GenotypeGVCFs to merge the samples from the same population in an unique vcf file as follow GenomeAnalysisTK.jar -T GenotypeGVCFs -R reference.fasta -stand_call_conf 20 -stand_emit_conf 20 -ploidy 6 --variant sample1.g.vcf --variant sample2.g.vcf --variant sample3.g.vcf (8 samples) -o output_HC.vcf Finally I'm going to filter the results with the Variant Filtration: GenomeAnalysisTK.jar -T VariantFiltration -R reference.fasta -V output.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o utputFiltered.vcf What do you think? Now I'd like to compare the two populations, but how??? manually in excel files? vcf tools seem not to handle ploidy higher than 2. Does anyone deal with these issues and can kindly give some tips? Best Marianna Created 2015-06-19 19:20:58 | Updated | Tags: haplotypecaller rnaseq genotypegvcfs gvcf Hello, I was wondering if there is a way to output all annotations for all sites when running HaplotypeCaller with BP_RESOLUTION. Currently it outputs all annotations for only called variants. Thanks in advance. Created 2015-05-28 03:46:02 | Updated | Tags: snp rnaseq Hai, I have Exome-seq and RNA-seq data and I try to find SNP in those sample. I know that the SNP is not in the gene itself but in the promoter region. From what I know, Exome-seq and RNA-seq do not cover promoter region. What is your suggestion about this? Probably you can share some expreience how to find SNP in promoter regiuon with RNA-seq and Exome-seq. data Thank you. Created 2015-05-05 02:51:05 | Updated | Tags: rnaseq I am using multiple RNA seq samples from same individual and genotyping for dbsnp locations. I need to get all 0/0 0/1 and 1/1 in my matrix for all samples whichever have reasonable coverage. Emitting all confident sites force the program looking at every possible site across all samples and also get 0/0 annotation while using unified genotyper. On the other hand since I want to just genotype DBSNPs is it fine to activate genotype given allele? Will I still get 0/0 It should the look like java -Xmx4g -jar /mnt/projects/senguptad/ctc/K562-allele/GenomeAnalysisTK.jar \ -T UnifiedGenotyper \ -R /mnt/projects/senguptad/ctc/hg19/hg19.fa \ --dbsnp /mnt/AnalysisPool/libraries/genomes/hg19/dbsnp/dbsnp_137.hg19.vcf \ -I /mnt/projects/senguptad/ctc/GLIO/GLIO/unique/newresult4/ready_readgrp_SRR1294973.bam \ -I /mnt/projects/senguptad/ctc/GLIO/GLIO/unique/newresult4/ready_readgrp_SRR1294974.bam \ . . . . --out /mnt/projects/senguptad/ctc/GLIO/GLIO/unique/newresult4/finalX.vcf \ -stand_call_conf 30.0 \ -stand_emit_conf 10.0 \ -gt_mode GENOTYPE_GIVEN_ALLELES --alleles /mnt/AnalysisPool/libraries/genomes/hg19/dbsnp/dbsnp_137.hg19.vcf -out_mode EMIT_ALL_CONFIDENT_SITES \ -l INFO \ -A HaplotypeScore \ -A InbreedingCoeff \ -glm SNP \ -nt 1 \ Am I correct? Created 2015-02-12 08:13:28 | Updated | Tags: dbsnp rnaseq singlecell In a project I need see the allelic frequency for dbSNPs in the RNAseq data of a single cell. To be precise, given the dbsnp I need 0/0s as well if there is required coverage of reads. SNV calling pipeline normally does not report if it is 0/0. I have come all the way to recalibrated BAM following the RNAseq SNV calling best practice as suggested in GATK site. Help with the command would be highly appreciated. Created 2015-01-21 19:53:26 | Updated | Tags: baserecalibrator haplotypecaller vcf bam merge rnaseq Hi, I am working with RNA-Seq data from 6 different samples. Part of my research is to identify novel polymorphisms. I have generated a filtered vcf file for each sample. I would like to now combine these into a single vcf. I am concerned about sites that were either not covered by the RNA-Seq analysis or were no different from the reference allele in some individuals but not others. These sites will be ‘missed’ when haplotypeCaller analyzes each sample individually and will not be represented in the downstream vcf files. When the files are combined, what happens to these ‘missed’ sites? Are they automatically excluded? Are they treated as missing data? Is the absent data filled in from the reference genome? Alternatively, can BaseRecallibrator and/or HaplotypeCaller simultaneously analyze multiple bam files? Is it common practice to combine bam files for discovering sequence variants? Created 2014-12-17 18:04:27 | Updated | Tags: haplotypecaller gatk error rnaseq genotyping genotyping-mode Hi, I'm currently trying to use GATK to call variants from Human RNA seq data So far, I've managed to do variant calling in all my samples following the GATK best practice guidelines. (using HaplotypeCaller in DISCOVERY mode on each sample separately) But I'd like to go further and try to get the genotype in every sample, of each variant found in at least one sample. This, to differentiate for each variant, samples where that variant is absent (homozygous for reference allele) from samples where it is not covered (and therefore note genotyped). To do so, I've first used CombineVariants to merge variants from all my samples and to create the list of variants to be genotype{ALLELES}.vcf

I then try to regenotype my samples with HaplotypeCaller using the GENOTYPE_GIVEN_ALLELES mode and the same settings as before: my command is the following:

**java -jar ${GATKPATH}/GenomeAnalysisTK.jar -T HaplotypeCaller -R${GENOMEFILE}.fa -I ${BAMFILE_CALIB}.bam --genotyping_mode GENOTYPE_GIVEN_ALLELES -alleles${ALLELES}.vcf -out_mode EMIT_ALL_SITES -dontUseSoftClippedBases -stand_emit_conf 20 -stand_call_conf 20
-o ${SAMPLE}_genotypes_all_variants.vcf -mbq 25 -L${CDNA_BED}.bed --dbsnp ${DBSNP}.vc**f In doing so I invariably get the same error after calling 0.2% of the genome. ##### ERROR ------------------------------------------------------------------------------------------ ##### ERROR stack trace java.lang.IndexOutOfBoundsException: Index: 3, Size: 3 at java.util.ArrayList.rangeCheck(ArrayList.java:635) at java.util.ArrayList.get(ArrayList.java:411) at htsjdk.variant.variantcontext.VariantContext.getAlternateAllele(VariantContext.java:845) at org.broadinstitute.gatk.tools.walkers.haplotypecaller.HaplotypeCallerGenotypingEngine.assignGenotypeLikelihoods(HaplotypeCallerGenotypingEngine.java:248) at org.broadinstitute.gatk.tools.walkers.haplotypecaller.HaplotypeCaller.map(HaplotypeCaller.java:1059) at org.broadinstitute.gatk.tools.walkers.haplotypecaller.HaplotypeCaller.map(HaplotypeCaller.java:221) at org.broadinstitute.gatk.engine.traversals.TraverseActiveRegions$TraverseActiveRegionMap.apply(TraverseActiveRegions.java:709) at org.broadinstitute.gatk.engine.traversals.TraverseActiveRegions$TraverseActiveRegionMap.apply(TraverseActiveRegions.java:705) at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.executeSingleThreaded(NanoScheduler.java:274) at org.broadinstitute.gatk.utils.nanoScheduler.NanoScheduler.execute(NanoScheduler.java:245) at org.broadinstitute.gatk.engine.traversals.TraverseActiveRegions.traverse(TraverseActiveRegions.java:274) at org.broadinstitute.gatk.engine.traversals.TraverseActiveRegions.traverse(TraverseActiveRegions.java:78) at org.broadinstitute.gatk.engine.executive.LinearMicroScheduler.execute(LinearMicroScheduler.java:99) at org.broadinstitute.gatk.engine.GenomeAnalysisEngine.execute(GenomeAnalysisEngine.java:319) at org.broadinstitute.gatk.engine.CommandLineExecutable.execute(CommandLineExecutable.java:121) at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:248) at org.broadinstitute.gatk.utils.commandline.CommandLineProgram.start(CommandLineProgram.java:155) at org.broadinstitute.gatk.engine.CommandLineGATK.main(CommandLineGATK.java:107) ##### ERROR ------------------------------------------------------------------------------------------ ##### ERROR A GATK RUNTIME ERROR has occurred (version 3.3-0-g37228af): ##### ERROR ##### ERROR This might be a bug. Please check the documentation guide to see if this is a known problem. ##### ERROR If not, please post the error message, with stack trace, to the GATK forum. ##### ERROR Visit our website and forum for extensive documentation and answers to ##### ERROR commonly asked questions http://www.broadinstitute.org/gatk ##### ERROR ##### ERROR MESSAGE: Index: 3, Size: 3 ##### ERROR ------------------------------------------------------------------------------------------ because the problem seemed to originate from getAlternateAllele, I tried to play with --max_alternate_alleles by setting it to 2 or 10, without success. I also checked my${ALLELES}.vcf file to look for malformed Alternate alleles in the region where the GATK crashes (Chr 1, somewhere after 78Mb) , but I couldn't identify any... (I searched for Alternate alles that would not match the following extended regexpr '[ATGC,]+')

Created 2014-12-11 18:37:39 | Updated | Tags: rnaseq cohort rna-seq

Dear GATK team,

Is there a value in cohort calling in RNA-Seq similar to what is recommended in the GATK DNA-Seq workflow? I am trying to understand why cohort calling is highly emphasized in DNA-Seq but not mentioned in the RNA-Seq workflow.

Thank you!

Joe

Created 2014-12-04 18:54:17 | Updated | Tags: haplotypecaller vcf rnaseq

Specifically, what does the 'start' component of this flag mean? Do the reads all have to start in exactly the same location? Alternatively, does the flag specify the total number of reads that must overlap a putative variant before that variant will be considered for calling?

Created 2014-11-25 14:57:50 | Updated | Tags: rnaseq

Hello,

I am running the SNP calling pipeline from RNA-seq data. I used STAR for alignment. My reference genome is composed of 32012 scaffolds from which more than 30000 are contigs with length of less than 1000 bp. I first decided to delete these contigs and just keep the superscaffolds, scaffolds and contigs longer than 1000 bp but in the STAR manual, it was recommended to keep all the sequences because other types of RNA can be mapped to them, so, I have retained them.

I have run the rest of the pipeline using these 32012 scaffolds as my reference. However, for the last step which is variant calling, I am going to extract only scaffolds that map to chromosome Z, so, I will have sthg around 40 scaffolds.

As I don't know the algorithms behind GATK and picard, I was wondering whether using these large numbers of primary scaffolds may be problematic at some steps? I am sorry for this general question, I have run the pipeline and have not encountered any problem so far but I was wondering if there is anything specific that I should look into the outputs to make sure everything has gone very well.

Created 2014-11-03 13:43:38 | Updated | Tags: haplotypecaller rnaseq pooled-calls

Hello,

First of all, thank you for your detailed best practice pipeline for SNP calling from RNA-seq data.

I have pooled RNA seq data which I need to call SNP from. Each library consists of a pooled sample of 2-3 individuals of the same sex-tissue combination.

I was wondering if Haplotype caller can handle SNP calling from pooled sequences or is it better if I use FreeBayes?

I understand that these results come from experimenting with the data but it would be great if you could share your experiences with me on this.

Cheers, Homa

Created 2014-10-07 12:35:50 | Updated | Tags: rnaseq variant-calling number samples

I have 90 exome samples coming from three sample groups. What would be the best idea to do variant calling using GATK? I am planning to call variant from three sample groups separately and and compare them. So, per group there are 30 samples; is this number of sample suffecient enough to merge BAM files and call variants ?

I also have RNASeq data from the same samples. Is it good idea to call variants from RNASeq data too ?

Created 2014-08-14 19:37:20 | Updated | Tags: best-practices rnaseq variant-calling

Hi,

Thank you for providing guidelines on RNA-Seq variant discovery. For our data, we are currently playing with multiple mapping methods and have noticed that 2-step alignments work "better" than 1-step alignments. By 2-step alignments, I mean using STAR as step1 and then take the unmapped from this and use another aligner (like Bowtie2) for alignment. If I use such a methodology, will there be an issue in variant calling when during splitting cigar strings I ask it convert the 255 MAPQ to another value (like 60 in the best practices example), since bowtie2 gives different MAPQ scores. Sorry if this seems like a stupid question, but I am just a little curious how such a thing might affect the variant calls. Any insights/comment on this will be greatly appreciated.

Thanks!

Created 2014-07-14 06:00:06 | Updated | Tags: multi-sample rnaseq

Hi there,

we are working on 454 RNaseq data......we have RNAseq data of four different tissues from six different individuals.... we want to call variants from all these data in a one job....but as per your recommendation we have to run each tissue data from each individual separately...so my query is can we join them as per the DNAseq guidelines or we have to find out some another way to do this kind of analysis.... Please help us if you have any suggestions to do Multisample Variant calling from RNAseq data.....

Best Regards

Created 2014-06-09 19:25:22 | Updated | Tags: haplotypecaller rnaseq stand-emit-conf stand-call-conf

I have processed samples using GATK 3.1 Haplotypecaller according to the RNA Seq best practice. Haplotypecaller is set with --stand_emit_conf = 20 and --stand_call_conf = 20 options, but I could find variants which QUAL less than 20 in the output gVcf file and variants which its QUAL < 20 is not marked as LowQual. I wonder what those parameters do for Haplotypecaller.

I have run with --stand_emit_conf = 20 and --stand_call_conf = 30 for testing. Output gvcf file appears to be identical.

Thanks!

Created 2014-03-12 15:22:35 | Updated | Tags: haplotypecaller error rnaseq

Hi,

I was trying to call variants in RNAseq data using GATK 3.0 when I got the following stack trace:

##### ERROR ------------------------------------------------------------------------------------------
##### ERROR stack trace
java.lang.NullPointerException
at org.broadinstitute.sting.gatk.traversals.TraverseActiveRegions$TraverseActiveRegionMap.apply(TraverseActiveRegions.java:708) at org.broadinstitute.sting.gatk.traversals.TraverseActiveRegions$TraverseActiveRegionMap.apply(TraverseActiveRegions.java:704)
at org.broadinstitute.sting.utils.nanoScheduler.NanoScheduler$ReadMapReduceJob.run(NanoScheduler.java:471) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:334) at java.util.concurrent.FutureTask.run(FutureTask.java:166) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
##### ERROR ------------------------------------------------------------------------------------------
##### ERROR A GATK RUNTIME ERROR has occurred (version nightly-2014-03-10-gf78001a):
##### ERROR
##### ERROR This might be a bug. Please check the documentation guide to see if this is a known problem.
##### ERROR If not, please post the error message, with stack trace, to the GATK forum.
##### ERROR
##### ERROR MESSAGE: Code exception (see stack trace for error itself)
##### ERROR ------------------------------------------------------------------------------------------

Here are the command line arguments:

Program Args: -T HaplotypeCaller -I in.bam -R ref.fa -o raw.snps.indels.vcf -nct 8 -recoverDanglingHeads -dontUseSoftClippedBases -stand_call_conf 20 -stand_emit_conf 20

As you can see, I got the error above from one of the nightly builds. Before that I also tried version 3.0-0-g6bad1c6, and this produced the exact same error. What's curious about this is that it didn't fail in the same place each time. I did this on 20 samples, and for the first run, 15 of the samples failed with this error. One of the samples failed after 7 minutes, so I decided to try that one again to see if I could reproduce it, but it went past the point (both in time and genomic position) where it failed the first time.

I decided to download a nightly build (version nightly-2014-03-10-gf78001a) and see if this had been fixed, but again, 15 of the samples failed. However, it was not the same set of samples that failed as with the other version.

The reads were aligned using STAR, and prior to this step I ran SplitNCigarReads and IndelRealigner.

Thanks, Niklas

Created 2013-10-24 11:31:46 | Updated 2013-10-24 11:38:15 | Tags: snp rnaseq snps mrnaseq

Hi! I have worked some time on a mRNAseq set, single-end. Its a high quality set and lots of biological replicates (200+).

My question is, how could I best contribute to the methodology used for SNPs call in mRNAseq? What do we need tested to improve this method?

Created 2013-06-12 16:09:40 | Updated | Tags: commandlinegatk workflow rnaseq

Hi all: I find that among all the work flows of GATK http://www.broadinstitute.org/gatk/guide/topic?name=methods-and-workflows there are no workflows for RNA-seq analysis. I understand that GATK mainly focuses on variant calling, can anyone tell me how to use GATK for RNA-seq analysis?

thanks daniel

Created 2013-06-10 04:38:43 | Updated | Tags: reducereads rnaseq

Hi,

I've been trying to get ReduceReads working in a pipeline I've made that incorporates GATK tools to call variants in RNA-seq data. After performing indel realignment and base recalibration I'm trying to use ReduceReads prior to calling variants using Unified Genotyper.

I've been using GATK version 2.3.9. When I try to use ReduceReads on a 1.7Gb .bam file, I need to set aside 100Gb memory to perform the operation for the process to complete (otherwise I'll get an error saying I didn't provide enough memory to run the program and to adjust the maximum heap size using the -Xmx option etc).

The problem isn't that ReduceReads doesn't work - it does, however of the 100Gb I set aside, it uses 80-90Gb of it. This means I can't run more than one job at a time due to the constraints of the machine I'm using etc.

I've been looking through the GATK forum and understand it may be a GATK version issue, though I've tried using GATK 2.5.2 ReduceReads for this step and it still requires 70-80Gb memory.

Can anyone provide any clues as to what I may be doing wrong? or whether I can do something to make it use less memory so I can run multiple jobs simultaneously?

The command I'm using is: